AI in Infrastructure Podcast

Mobile networks were built for predictable human behavior, not the nonstop demands of agentic and physical AI. In this episode, Pallavi Mahajan, Chief Technology and AI Officer at Nokia, examines why AI workloads demand deterministic connectivity rather than traditional reliability benchmarks, and how networks are shifting from passive transport to active participants in AI decision-making. The conversation covers the shift from over-provisioning to AI-native network intelligence, the move toward distributed compute at the edge, and new capabilities like integrated sensing and real-time translation built directly into network infrastructure. If your company sells AI products or services to enterprises, reaching the right buyers takes more than targeting the right job titles. You need senior leaders at relevant organizations who are ready to act. Emerj reaches VP-level and senior enterprise decision-makers actively exploring how AI can transform their operations. Learn how leading AI companies use Emerj to build market authority and connect with qualified enterprise buyers at emerj.com/AD4

AI pilots that perform flawlessly in testing often stall the moment they reach production, and enterprise leaders are left wondering why. In this episode, Samar Abbas, Co-founder and CEO at Temporal, examines why long-running AI agents turn state and context management into the central engineering challenge, and how durable execution lets developers write code that runs to completion despite failures. He outlines what leaders should build now: a foundation that combines well-defined business processes with steps handed to LLMs, keeps organizations free to switch models, clouds, and frameworks, and supports reliable, governed environments for running agents at enterprise scale. ​ If you want to be considered, download our media kit at emerj.com/AD4

Enterprises are past asking whether LLMs work; the harder question is how to run them at scale without becoming dependent on a single model, cloud, or chip vendor — and without running out of power or data-center capacity in the process. In this episode, Rodrigo Liang, Co-founder and CEO of SambaNova, examines why sovereignty and business continuity now anchor enterprise infrastructure strategy, why regulated firms are keeping customer data inside their own firewalls as a first step, and why the enterprises moving fastest are planning for 100% of the business to be AI-enabled rather than gating use cases one at a time, with host Daniel Faggella, Emerj CEO and Head of Research. ​ Liang outlines the practical shifts underway: from single-vendor stacks toward breadth across models, clouds, and chip architectures, from black-box outputs toward agentic workflows with checkable steps, and from scattered pilots toward code generation as the first use case that reveals what AI at scale will cost. ​ To go deeper on this topic and learn how leading organizations approach AI investment more like a venture portfolio, and why interdisciplinary collaboration is critical to defining the right data for AI success, download our free PDF report, "Beginning with AI," at emerj.com/aik4

The enterprise shift at the center of this episode is the growing gap between AI ambition and the underlying data and context infrastructure required to run systems reliably at scale. In this episode, Dr. Ali Alkhafaji, Chief Executive Officer at Apply Digital, examines how enterprises stall when they focus on adding AI to existing workflows rather than defining the outcomes they want to achieve, as he joins Emerj's Marilie Fouché. He highlights the practical moves that matter most — strengthening data foundations, building organizational context into AI systems, and driving adoption through top‑down leadership and targeted enablement. Learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale, download our media kit at http://emerj.com/AD4

General‑purpose AI tools are hitting reliability and accuracy limits in professional workflows, forcing enterprises to rethink where these systems can truly deliver operational value. In this episode, David Wong, Chief Product Officer at Thomson Reuters, examines how domain‑specific data, expert‑trained decision patterns, and integrated software enable AI to perform professional‑grade work, in conversation with host Matthew DeMello. The discussion highlights the practical shift toward targeted deployment, the four‑pillar framework for agentic systems, and the conditions required for trustworthy automation in legal, tax, and compliance environments. ​ Learn how brands work with Emerj and other Emerj Media options at emerj.com/partner

The shift from guarded, siloed data access to fully agentic, end‑to‑end AI workflows is forcing enterprises to rethink how accuracy, access control, and security must operate when anyone can query sensitive information through natural language. In this episode, Anahita Tafvizi, Chief Data & AI Officer at Snowflake, joins Daniel Faggella, Emerj CEO and Head of Research, to examine how robust governance, role‑based access control, and embedded data teams enable trustworthy AI agents that automate high‑value workflows across finance, sales, HR, and other regulated functions. The discussion highlights practical decisions around securing new points of entry, standardizing metrics, and designing repeatable agentic workflows that move enterprises from systems of record to systems of action. To listen to the conversations other infrastructure and AI leaders in the Fortune 500 are tuned into, subscribe to the AI infrastructure podcast at emerj.com/inf1

Legacy enterprises are being forced to rethink how they modernize data, infrastructure, and operating models as agentic systems begin reshaping workflows once defined entirely by humans. In this episode, Matt Renner, President and Chief Revenue Officer at Google Cloud, joins host Daniel Faggella and examines how AI maturity, data modernization, and agent orchestration are becoming the new backbone of enterprise transformation. The discussion highlights practical shifts in executive fluency, data strategy, orchestration platforms, and the processes required to move from isolated pilots to scalable agentic operations. To learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale, download our media kit at http://emerj.com/AD4

Uptime expectations in energy and infrastructure environments are tightening, while the skilled‑trades gap and rising asset complexity are making traditional, date‑based maintenance models increasingly unsustainable. In this episode, Joe Lang, Vice President of Service Technology and Innovation at Comfort Systems USA, examines how real‑time equipment data, technician‑centric tooling, and disciplined asset organization are becoming essential to operating reliably in zero‑tolerance environments — in conversation with host Yolandi de Weerdt. He highlights the practical shifts required to move from reactive to prescriptive maintenance, including structuring asset data, capturing accurate field information, and properly resourcing modernization efforts across distributed service teams. This episode is sponsored by Aquant. Learn how to identify real AI trends by tracking where venture funding is flowing, and by listening to how leading CEOs describe risk and competitive strategy. Download our free PDF report, "3 Ways to Discover AI Trends in Any Sector" at emerj.com/ait4

AI is moving into the physical economy, and enterprises are now confronting the infrastructure decisions required to support model‑driven systems in logistics, manufacturing, transportation, and other asset‑heavy environments. In this episode, Drew Henry, Executive Vice President for Physical AI at Arm Holdings, joins Emerj CEO Daniel Faggella to examine how leaders can redesign compute and hardware architectures for safety‑critical operations where power, reliability, and integration constraints are non‑negotiable. The discussion focuses on upgrading legacy automation, selecting the right compute for varied AI workloads, and using large‑scale simulation to validate changes before retooling physical systems. Emerj reaches over a million senior operators and AI decision‑makers each year — see how leading AI brands turn that attention into qualified pipeline at emerj.com/AD1